Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Learning Distance Metrics for Multi-Label Classification

      Gouk, Henry; Pfahringer, Bernhard; Cree, Michael J.
      Thumbnail
      Files
      Pfahringer & Cree.pdf
      Published version, 1.177Mb
      Link
       www.jmlr.org
      Citation
      Export citation
      Gouk, H., Pfahringer, B., & Cree, M. J. (2016). Learning Distance Metrics for Multi-Label Classification. In B. Durrant & K.-E. Kim (Eds.), Proceedings of The 8th Asian Conference on Machine Learning (Vol. 63, pp. 318–333).
      Permanent Research Commons link: https://hdl.handle.net/10289/10898
      Abstract
      Distance metric learning is a well studied problem in the field of machine learning, where it is typically used to improve the accuracy of instance based learning techniques. In this paper we propose a distance metric learning algorithm that is specialised for multi-label classification tasks, rather than the multiclass setting considered by most work in this area. The method trains an embedder that can transform instances into a feature space where squared Euclidean distance provides an estimate of the Jaccard distance between the corresponding label vectors. In addition to a linear Mahalanobis style metric, we also present a nonlinear extension that provides a substantial boost in performance. We show that this technique significantly improves upon current approaches for instance based multi-label classification, and also enables interesting data visualisations.
      Date
      2016
      Type
      Conference Contribution
      Rights
      © 2016 H. Gouk, B. Pfahringer & M. Cree.
      Collections
      • Computing and Mathematical Sciences Papers [1455]
      • Science and Engineering Papers [3122]
      Show full item record  

      Usage

      Downloads, last 12 months
      54
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement